Adaptive Color Thresholding for Image Processing
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Solution Overview
Problem
Existing image processing methods struggle to accurately classify pixels near color boundaries in images, leading to blurring and incorrect color representation, especially in lossy compression and noise-affected areas.
Innovation Solution
An image processing apparatus that sets an influence value for each pixel based on color difference and variation within a local region, using a threshold value to determine pixel classification into representative color regions, and updates representative colors considering influence values and boundaries between colors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed threshold value is used for color classification, then the processing is simple and fast, but pixels near color boundaries are misclassified causing blurring and incorrect color representation
Solution Approach 1:
The patent applies the dynamics principle by making the threshold value adaptive rather than fixed. The threshold is dynamically adjusted based on the local color characteristics of each pixel's neighborhood, allowing the classification criterion to change according to the specific image content. This resolves the contradiction by enabling both fast processing (through efficient local statistics calculation) and accurate classification (through context-aware adaptive thresholds).
Solution Approach 2:
The patent implements local quality by using different threshold values for different regions of the image. Instead of applying a global fixed threshold, the method calculates local color statistics (mean and standard deviation) for each pixel's neighborhood and uses these to determine region-specific thresholds. This allows accurate color boundary detection in complex regions while maintaining processing efficiency, resolving the contradiction between simplicity and accuracy.
2Device complexity
If representative colors are used to limit colors in an image, then the color palette is simplified, but color boundaries become blurred and color integrity is lost
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the classification threshold based on local color statistics. The threshold parameter is modified according to the standard deviation of colors in each pixel's neighborhood, allowing the system to maintain sharp color boundaries where needed while still achieving color palette simplification. This resolves the contradiction by making the threshold parameter adaptive rather than fixed.
Solution Approach 2:
The patent implements local quality by applying different classification criteria to different regions. Pixels in homogeneous regions use one classification approach, while pixels near color boundaries use an adaptive threshold based on local color variation. This ensures that color boundaries are preserved with high precision while still achieving overall color limitation, resolving the contradiction between color palette simplification and boundary definition accuracy.
3Productivity
If colors are incorporated into representative colors without considering local variation, then the color limitation is achieved, but noise and compression artifacts are amplified
Solution Approach 1:
The patent applies parameter changes by using the standard deviation of local color regions as a dynamic parameter to adjust the classification threshold. This allows the system to adapt to local noise and compression artifacts by modifying the threshold based on actual local variation rather than applying a fixed global threshold. This resolves the contradiction by making the color limitation process robust to local anomalies while maintaining overall efficiency.
Solution Approach 2:
The patent implements local quality by analyzing and treating each pixel's neighborhood independently. By calculating local color statistics and using these to determine classification thresholds, the method can distinguish between legitimate color variation and noise/artifacts. This allows efficient color limitation while maintaining reliability, as the local analysis prevents noise amplification that would occur with global fixed-threshold methods.
Data Source
AI summary
An image processing apparatus includes an influence value setting unit that sets an influence value on a pixel of interest in a local region of an image, the influence value being a color difference in the local region, and a color region processing unit that treats the pixel of interest as a pixel in a region of a representative color when a color of the pixel of interest is within a range from the representative color to the threshold value, the threshold value being set such that the threshold value increases as the influence value increases.